Cross Sensor Iris and Periocular Database

The CSIP Dataset

The Cross Sensor Iris and Periocular Dataset.

50 Participants

10 Mobile Setups

2000+ Images

+ Ground-Truth

When attempting to perform iris or periocular biometrics on mobile environments, several problems arise: 1) the wide variety of camera sensors and lenses mobile phones and tablets come equipped with produce discrepancies in working images, as they are acquired with color distortions, at multiple resolutions, etc.; 2) on-the-go acquisition by potentially untrained subjects will result in demanding Pose, Illumination and Expression (PIE) changes, as not all users hold their mobile devices at the same position, resulting in varying acquisition angles and scales, or rotated images; the acquisition environment can have poor or insufficient lighting, and uncontrolled outdoor daylight will most likely produce spectacle reflections over the iris region; 3) etc.

The main objective of the CSIP database was to gather images from a representative group of participants, acquired over cross-sensor setups and varying acquisition scenarios, thus mimicking the conditions faced on mobile application scenarios. Along with the data acquired with different mobile devices, an iris segmentation mask is also provided, allowing assessing the performance of both iris and periocular segmentation and recognition algorithms on mobile environments.

Mobile devices used for acquisition

Xperia Arc S

Sony Ericsson Xperia Arc S running Android 2.3.4. Images acquired using the standard camera application, with resolution 3264x2448, with and without flash.

Apple iPhone 4

Apple iPhone 4 running iOS 7.1. Images acquired using the standard camera application, resolutions 2592x1936 (rear) and 640x480 (frontal), with and without flash.

THL W200

THL W200 running Android 4.2.1. Images acquired using the standard camera application, resolutions 3264x2448 (rear) and 2593x1920 (frontal), with and without flash.

Huawei U8510

Huawei Ideos X3 (U8510) running Android 2.3.3. Images acquired using the standard camera application, resolutions 2048x1536 (rear) and 640x480 (frontal), without flash.

The Imaging Setup

Considering the heterogeneity of camera sensor/lens setups consumer mobile devices can deliver, a total of 10 different setups were used during the dataset acquisition stage: four different devices, some of them with frontal and rear cameras and LED flash. Aiming at mimicking the variability of noise factors associated with on-the-go recognition, participants were not imaged at a single particular location, but on multiple sites, as they were, with artificial, natural and mixed illumination conditions. As so, there is a substantial difference between each acquisition setup and surrounding conditions, even when the same setup was used to capture images from different subjects. From visual inspection, eight different noise factors are distinguishable, and can affect the biometric recognition process: multiple scales; chromatic distortions; image rotation; poor lighting; off-angle acquisition; out-of-focus images; deviated gaze; and iris obstructions (including reflexions).

Ground-Truth Data

For each periocular image acquired by the mobile devices, a binary iris segmentation mask is provided with the CSIP dataset. Those masks were automatically obtained using a state-of-the-art iris segmentation approach particularly suitable for uncontrolled acquisition conditions, which has been corroborated by the first place achieved at the Noisy Iris Challenge Evaluation - Part 1 (NICE.I)1.


Please cite the following paper if you use this dataset.

Paper 1
Gil Santos, Emanuel Grancho, Marco V. Bernardo and Paulo T. Fiadeiro;
Fusing Iris and Periocular Information for Cross-sensor Recognition,
Pattern Recognition Letters, DOI: 10.1016/j.patrec.2014.09.012, 2014.

Over the last years the usage of mobile devices has substantially grown, along with their capabilities and applications. Extending biometric technologies to such gadgets is quite desirable, as it would rep- resent the ability to perform biometric recognition virtually anytime, anywhere, and by everyone. This paper focus on biometric recognition on mobile environments using the iris and periocular information as main traits, and its main contributions are three-fold: 1) announce the availability of an iris and periocular dataset containing images acquired with 10 different mobile setups, along with the corresponding iris segmentation data. Such dataset allows to evaluate both iris segmentation and recognition methods, as well as periocular recognition techniques; 2) report the outcomes of device-specific calibration techniques that compensate for the different color perception inherent to each setup; 3) propose the application of well-known iris and periocular recognition strategies, based on classical encoding and matching techniques, giving evidence on how they can be fused to overcome the issues associated with mobile environments.


The CSIP Dataset provided here is for non-commercial research/educational use only.

Access request to the CSIP database must be directed (by email) to one of the e-mails in the contact section.
Applicants should manually fill, sign, scan and attach the application form to the given email address.
Upon receipt of an executed copy of the signed application form, access instructions will be given.
For faster processing of your request, please be sure to use your institutional e-mail to send the form.

Image Gallery

2004 images, depicting 50 subjects over 10 different mobile setups.

Segmentation Masks

Black and white iris segmentation masks, one for each image on the gallery.

Annotation Files

XML files detailing participant ID and information on acquisition conditions.

Contact Us

Want to get in touch? Come see our lab, or drop us an e-mail.

Our Location

SOCIA Lab - Soft Computing and Image Analysis Lab
Department of Computer Science
University of Beira Interior
Rua Marquês D'Ávila e Bolama
6201-001 Covilhã (Portugal)


Gil Santos

Paulo T. Fiadeiro


This work could not have been accomplished without the support from our sponsors.